Soft Data Space/Time Mapping of Coarse Particulate Matter Annual Arithmetic Average Over the U.S

Abstract

In the U.S., particulate matter (PM10) is considered an important criteria air pollutant and it is monitored throughout the country by means of a considerably dense network of stations. Because of the health risks associated with PM10, it is important to study carefully the spatiotemporal distribution of the air pollutant. In the last decade, the modern BME approach has emerged as an advanced function of temporal GIS (TGIS). The BME approach has certain powerful features and has been used for mapping PM10 and PM2.5 distributions in the U.S. and abroad. In this work we propose an approach to use available information to develop probabilistic soft data about the annual arithmetic average of PM10, and we use the BME framework to rigorously process that information and produce realistic spatiotemporal maps of PM10 distribution over the US. We apply the approach presented on a large PM10 dataset from the USEPA AIRS database covering the 1984 to 2000 period.